Due to non-orthogonal resource allocation, intra-cell interference always exists in NOMA networks compared to OMA networks, which usually leads to nonconvex resource allocation problems.
Traditional optimization methods, which are used to solve the nonconvex problems for optimizing the performance of NOMA networks, mostly operate in an offline manner with high computation complexity and depend largely on accurate CSI [1]}, [2]}, [3]}, [4]}.
Machine learning tools [5]}, [6]}, [7]}, [8]} can exploit big data analytics for wireless network state estimation and find the relationship between the optimized variables and objective functions in an online manner so as to reduce the computational complexity for solving the nonconvex problems in NOMA.
However, given that multi-cell NOMA needs global CSI,
centralized learning algorithm may require the BSs to continuously upload their collected data to a centralized processing server, which can lead to a high network overhead and significant delays.
Besides, in NOMA, each subcarrier can be occupied by multiple users. In consequence, using a centralized learning algorithm for resource management or network control may need a large number of iterations to converge.
Thus, centralized machine learning algorithms such as in [9]}, [10]}, [11]}, [12]} will not be able to handle the resource allocation, signal detection and user behaviour prediction problems in NOMA.
For NOMA, FL have two important use cases: 1) FRL can be used to solve complex convex and nonconvex optimization problems that arise in various NOMA use cases such as network control, user clustering, resource management and interference alignment and 2) FSL enables users to collaboratively learn a shared prediction model while remaining their collected data on their devices
for user detection and CSI prediction.
